Selected Applications of Machine Learning in Pediatrics and Pediatric Radiology

Needs Statement

Attending physicians, residents, fellows, nurses, graduate students, medical students, analysts and programmers need to be regularly updated on the role of informatics in health care, the current approaches to utilize large sets of data in health care and appropriate measures to draw effective conclusions from this data. The series aims to increase knowledge of the learners in these topic areas, and to develop better means to put data to effective use in order to enhance the quality of health care and outcomes

Educational Objectives

At the conclusion of the session, the participants should be able to:

• Name major classes of machine learning algorithms of use in a pediatric healthcare context.
• Identify strengths and weaknesses of machine learning algorithms applied in a pediatric healthcare context.
• Recognize opportunities for machine learning applications in a pediatric healthcare context.
• Evaluate the accuracy of a machine learning algorithm applied to a pediatric healthcare problem.
• Construct the framework for a machine learning algorithm for a specific healthcare problem.

Target Audience

Accreditation/Credit Designation

Baylor College of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

Baylor College of Medicine designates this enduring material activity for a maximum of 1.0 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Disclosure Policy

Baylor College of Medicine (BCM) is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education (CME) for physicians. BCM is committed to sponsoring CE activities that are scientifically based, accurate, current, and objectively presented.

In accordance with the ACCME Standards for Commercial Support, BCM has implemented a mechanism requiring everyone in a position to control the content of an educational activity (i.e., directors, planning committee members, faculty) to disclose any relevant financial relationships with commercial interests (drug/device companies) and manage/resolve any conflicts of interest prior to the activity. Individuals must disclose to participants the existence or non-existence of financial relationships at the time of the activity or within 24 months prior.

In addition, BCM has requested activity faculty/presenters to disclose to participants any unlabeled use or investigational use of pharmaceutical/device products; to use scientific or generic names (not trade names) in referring to products; and, if necessary to use a trade name, to use the names of similar products or those within a class. Faculty/presenters have also been requested to adhere to the ACCME's validation of clinical content statements.

BCM does not view the existence of financial relationships with commercial interests as implying bias or decreasing the value of a presentation. It is up to participants to determine whether the relationships influence the activity faculty with regard to exposition or conclusions. If at any time during this activity you feel that there has been commercial/promotional bias, notify the Activity Director or Activity Coordinator. Please answer the questions about balance and objectivity in the activity evaluation candidly.

All of the relevant financial relationships listed for these individuals have been mitigated.